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| Package | Type | Spec |
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| ssizeRNA 1.3.3 CRAN · 2026-05-30 | Imports | ssize.fdr |
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Help for package ssize.fdr const macros = { "\\R": "\\textsf{R}", "\\mbox": "\\text", "\\code": "\\texttt"}; function processMathHTML() { var l = document.getElementsByClassName('reqn'); for (let e of l) { katex.render(e.textContent, e, { throwOnError: false, macros }); } return; } Package {ssize.fdr} Contents ssize.fdr-package ssize.F ssize.Fvary ssize.oneSamp ssize.oneSampVary ssize.twoSamp ssize.twoSampVary Type: Package Title: Sample Size Calculations for Microarray Experiments Version: 1.3 Author: Megan Orr [aut, cre], Peng Liu [aut] Maintainer: Megan Orr <megan.orr@ndsu.edu> Description: Functions that calculate appropriate sample sizes for one-sample t-tests, two-sample t-tests, and F-tests for microarray experiments based on desired power while controlling for false discovery rates. For all tests, the standard deviations (variances) among genes can be assumed fixed or random. This is also true for effect sizes among genes in one-sample and two sample experiments. Functions also output a chart of power versus sample size, a table of power at different sample sizes, and a table of critical test values at different sample sizes. License: GPL-3 Encoding: UTF-8 NeedsCompilation: no Packaged: 2022-06-06 06:11:56 UTC; megan Repository: CRAN Date/Publication: 2022-06-07 04:30:02 UTC Sample Size Calculations for Microarray Experiments Description This package calculates appropriate sample sizes for one-sample, two-sample, and multi-sample microarray experiments for a desired power of the test. Sample sizes are calculated under controlled false discovery rates and fixed proportions of non-differentially expressed genes. Outputs a graph of power versus sample size. Details Package: ssize.fdr Type: Package Version: 1.3 Date: 2022-06-05 License: GPL-3 For all functions, the user inputs the desired power, the false discovery rate to be controlled, the proportion(s) of non- differentially expressed genes, and the maximum possible sample size to be used in calculations. If the user inputs a vector of proportions of non-differentially expressed genes, samples size calculations are performed for each proportion. For the function ssize.twoSamp , the user must additionally input the common difference in mean treatment expressions as well as the common standard deviation for all genes. This becomes the common effect size and common standard deviation for all genes when using the function ssize.oneSamp . For the function ssize.twoSampVary ( ssize.oneSampVary ) the differences in mean treatment expressions (effect sizes) are assumed to follow a normal distribution and the variances among genes are assumed to follow an inverse gamma distribution, so parameters for these distributions must be entered. For the function ssize.F , the design matrix of the experiment, the parameter vector, and an optional coefficient matrix or vector of linear contrasts of interest must also be entered. The function ssize.Fvary allows the variances of the genes to follow an inverse gamma distribution, so the shape and scale parameters must be specified by the user. Author(s) Megan Orr <megan.orr@ndsu.edu>, Peng Liu <pliu@iastate.edu> References Liu, Peng and J. T. Gene Hwang. 2007. Quick calculation for sample size while controlling false discovery rate with application to microarray analysis. Bioinformatics 23(6): 739-746. Examples a<-0.05 ##false discovery rate to be controlled pwr<-0.8 ##desired power p0<-c(0.5,0.9,0.95) ##proportions of non-differentially expressed genes N<-20; N1<-35 ##maximum sample size for calculations ##Example of function ssize.oneSamp d<-1 ##effect size s<-0.5 ##standard deviation os<-ssize.oneSamp(delta=d,sigma=s,fdr=a,power=pwr,pi0=p0,maxN=N,side="two-sided") os$ssize ##first sample sizes to reach desired power os$power ##calculated power for each sample size os$crit.vals ##calculated critical value for each sample size ##Example of function ssize.oneSampVary dm<-2; ds<-1 ##the effect sizes of the genes follow a Normal(2,1) distribution alph<-3; beta<-1 ##the variances of the genes follow an Inverse Gamma(3,1) distribution. osv<-ssize.oneSampVary(deltaMean=dm,deltaSE=ds,a=alph,b=beta,fdr=a,power=pwr, pi0=p0,maxN=N1,side="two-sided") osv$ssize ##first sample sizes to reach desired power osv$power ##calculated power for each sample size osv$crit.vals ##calculated critical value for each sample size ##Example of function ssize.twoSamp ##Calculates sample sizes for two-sample microarray experiments ##See Figure 1.(a) of Liu & Hwang (2007) d1<-1 ##difference in differentially expressed genes to be detected s1<-0.5 ##standard deviation ts<-ssize.twoSamp(delta=d1,sigma=s1,fdr=a,power=pwr,pi0=pi,maxN=N,side="two-sided") ts$ssize ##first sample sizes to reach desired power ts$power ##calculated power for each sample size ts$crit.vals ##calculated critical value for each sample size ##Example of function ssize.twoSampVary ##Calculates sample sizes for multi-sample microarray experiments in which both the differences in ##expressions between treatments and the standard deviations vary among genes. ##See Figure 3.(a) of Liu & Hwang (2007) dm<-2 ##mean parameter of normal distribution of differences ##between treatments among genes ds<-1 ##standard deviation parameter of normal distribution ##of differences between treatments among genes alph<-3 ##shape parameter of inverse gamma distribution followed ##by standard deviations of genes beta<-1 ##scale parameter of inverse gamma distribution followed ##by standard deviations of genes tsv<-ssize.twoSampVary(deltaMean=dm,deltaSE=ds,a=alph,b=beta, fdr=a,power=pwr,pi0=p0,maxN=N1,side="two-sided") tsv$ssize ##first sample sizes to reach desired power tsv$power ##calculated power for each sample size tsv$crit.vals ##calculated critical value for each sample sizesv ##Example of function ssize.F ##Sample size calculation for three-treatment loop design microarray experiment ##See Figure S2. of Liu & Hwang (2007) des<-matrix(c(1,-1,0,0,1,-1),ncol=2,byrow=FALSE) ##design matrix of loop design experiment b<-c(1,-0.5) ##difference between first two treatments is 1 and ##second and third treatments is -0.5 df<-function(n){3*n-2} ##degrees of freedom for this design is 3n-2 s<-1 ##standard deviation p0.F<-c(0.5,0.9,0.95,0.995) ##proportions of non-differentially expressed genes ft<-ssize.F(X=des,beta=b,dn=df,sigma=s,fdr=a,power=pwr,pi0=p0.F,maxN=N) ft$ssize ##first sample sizes to reach desired power ft$power ##calculated power for each sample size ft$crit.vals ##calculated critical value for each sample sizeft$ssize ##Example of function ssize.Fvary ##Sample size calculation for three-treatment loop design microarray experiment des<-matrix(c(1,-1,0,0,1,-1),ncol=2,byrow=FALSE) ##design matrix of loop design experiment b<-c(1,-0.5) ##difference between first two treatments is 1 and ##second and third treatments is -0.5 df<-function(n){3*n-2} ##degrees of freedom for this design is 3n-2 alph<-3;beta<-1 ##variances among genes follow an Inverse Gamma(3,1) a1<-0.05 ##fdr to be fixed p0.F<-c(0.9,0.95,0.995) ##proportions of non-differentially expressed genes ftv<-ssize.Fvary(X=des,beta=b,dn=df,a=alph,b=beta,fdr=a1,power=pwr,pi0=p0,maxN=N1) ftv$ssize ##first sample sizes to reach desired power ftv$power ##calculated power for each sample size ftv$crit.vals ##calculated critical value for each sample sizeft$ssize Sample Size Calculations for Multi-Sample Microarray Experiments Description Calculates appropriate sample sizes for multi-sample microarray experiments for a desired power. Sample size calculations are performed at controlled false discovery rates and user-specified proportions of non-differentially expressed genes, design matrix, and standard deviation. A graph of power versus sample size is created. Usage ssize.F(X, beta, L = NULL, dn, sigma, fdr = 0.05, power = 0.8, pi0 = 0.95, maxN = 20, cex.title=1.15, cex.legend=1) Arguments X design matrix of experiment beta parameter vector L coefficient matrix or vector for linear contrastCalculates appropriate sample sizes for multi-sample microarray experiments for a desired power. Sample size calculations are performed at controlled false discovery rates and user-specified proportions of non-differentially expressed genes, design matrix, and standard deviation. A graph of power versus sample size is created.
ssize.F(X, beta, L = NULL, dn, sigma, fdr = 0.05, power = 0.8, pi0 = 0.95, maxN = 20, cex.title=1.15, cex.legend=1)##Sample size calculation for three-treatment loop design microarray experiment ##See Figure S2 of Liu & Hwang (2007) des<-matrix(c(1,-1,0,0,1,-1),ncol=2,byrow=FALSE) ##design matrix of loop design experiment b<-c(1,-0.5) ##difference between first two treatments is 1 and #second and third treatments is -0.5 df<-function(n)3*n-2 ##degrees of freedom for this design is 3n-2 s<-1 ##standard deviation a<-0.05 ##false discovery rate to be controlled pwr1<-0.8 ##desired power p0<-c(0.5,0.9,0.95,0.995) ##proportions of non-differentially expressed genes N1<-20 ##maximum sample size for calculations ft<-ssize.F(X=des,beta=b,dn=df,sigma=s,fdr=a,power=pwr1,pi0=p0,maxN=N1) ft$ssize ##first sample sizes to reach desired power for each proportion of #non-differentially expressed genes ft$power ##power for each sample size ft$crit.vals ##critical value for each sample sizeThis package calculates appropriate sample sizes for one-sample, two-sample, and multi-sample microarray experiments for a desired power of the test. Sample sizes are calculated under controlled false discovery rates and fixed proportions of non-differentially expressed genes. Outputs a graph of power versus sample size.
a<-0.05 ##false discovery rate to be controlled pwr<-0.8 ##desired power p0<-c(0.5,0.9,0.95) ##proportions of non-differentially expressed genes N<-20; N1<-35 ##maximum sample size for calculations ##Example of function ssize.oneSamp d<-1 ##effect size s<-0.5 ##standard deviation os<-ssize.oneSamp(delta=d,sigma=s,fdr=a,power=pwr,pi0=p0,maxN=N,side="two-sided") os$ssize ##first sample sizes to reach desired power os$power ##calculated power for each sample size os$crit.vals ##calculated critical value for each sample size ##Example of function ssize.oneSampVary dm<-2; ds<-1 ##the effect sizes of the genes follow a Normal(2,1) distribution alph<-3; beta<-1 ##the variances of the genes follow an Inverse Gamma(3,1) distribution. osv<-ssize.oneSampVary(deltaMean=dm,deltaSE=ds,a=alph,b=beta,fdr=a,power=pwr, pi0=p0,maxN=N1,side="two-sided") osv$ssize ##first sample sizes to reach desired power osv$power ##calculated power for each sample size osv$crit.vals ##calculated critical value for each sample size ##Example of function ssize.twoSamp ##Calculates sample sizes for two-sample microarray experiments ##See Figure 1.(a) of Liu & Hwang (2007) d1<-1 ##difference in differentially expressed genes to be detected s1<-0.5 ##standard deviation ts<-ssize.twoSamp(delta=d1,sigma=s1,fdr=a,power=pwr,pi0=pi,maxN=N,side="two-sided") ts$ssize ##first sample sizes to reach desired power ts$power ##calculated power for each sample size ts$crit.vals ##calculated critical value for each sample size ##Example of function ssize.twoSampVary ##Calculates sample sizes for multi-sample microarray experiments in which both the differences in ##expressions between treatments and the standard deviations vary among genes. ##See Figure 3.(a) of Liu & Hwang (2007) dm<-2 ##mean parameter of normal distribution of differences ##between treatments among genes ds<-1 ##standard deviation parameter of normal distribution ##of differences between treatments among genes alph<-3 ##shape parameter of inverse gamma distribution followed ##by standard deviations of genes beta<-1 ##scale parameter of inverse gamma distribution followed ##by standard deviations of genes tsv<-ssize.twoSampVary(deltaMean=dm,deltaSE=ds,a=alph,b=beta, fdr=a,power=pwr,pi0=p0,maxN=N1,side="two-sided") tsv$ssize ##first sample sizes to reach desired power tsv$power ##calculated power for each sample size tsv$crit.vals ##calculated critical value for each sample sizesv ##Example of function ssize.F ##Sample size calculation for three-treatment loop design microarray experiment ##See Figure S2. of Liu & Hwang (2007) des<-matrix(c(1,-1,0,0,1,-1),ncol=2,byrow=FALSE) ##design matrix of loop design experiment b<-c(1,-0.5) ##difference between first two treatments is 1 and ##second and third treatments is -0.5 df<-function(n)3*n-2 ##degrees of freedom for this design is 3n-2 s<-1 ##standard deviation p0.F<-c(0.5,0.9,0.95,0.995) ##proportions of non-differentially expressed genes ft<-ssize.F(X=des,beta=b,dn=df,sigma=s,fdr=a,power=pwr,pi0=p0.F,maxN=N) ft$ssize ##first sample sizes to reach desired power ft$power ##calculated power for each sample size ft$crit.vals ##calculated critical value for each sample sizeft$ssize ##Example of function ssize.Fvary ##Sample size calculation for three-treatment loop design microarray experiment des<-matrix(c(1,-1,0,0,1,-1),ncol=2,byrow=FALSE) ##design matrix of loop design experiment b<-c(1,-0.5) ##difference between first two treatments is 1 and ##second and third treatments is -0.5 df<-function(n)3*n-2 ##degrees of freedom for this design is 3n-2 alph<-3;beta<-1 ##variances among genes follow an Inverse Gamma(3,1) a1<-0.05 ##fdr to be fixed p0.F<-c(0.9,0.95,0.995) ##proportions of non-differentially expressed genes ftv<-ssize.Fvary(X=des,beta=b,dn=df,a=alph,b=beta,fdr=a1,power=pwr,pi0=p0,maxN=N1) ftv$ssize ##first sample sizes to reach desired power ftv$power ##calculated power for each sample size ftv$crit.vals ##calculated critical value for each sample sizeft$ssizeCalculates appropriate sample sizes for one-sample microarray experiments for a desired power. Sample size calculations are performed at controlled false discovery rates and user-specified proportions of non-differentially expressed genes, effect size, and standard deviation. A graph of power versus sample size is created.
ssize.oneSamp(delta, sigma, fdr = 0.05, power = 0.8, pi0 = 0.95, maxN = 35, side = "two-sided", cex.title=1.15, cex.legend=1)d<-2 ##effect size s<-1 ##standard deviation a<-0.05 ##false discovery rate to be controlled pwr<-0.8 ##desired power p0<-c(0.5,0.9,0.95) ##proportions of non-differentially expressed genes N<-20 ##maximum sample size for calculations os<-ssize.oneSamp(delta=d,sigma=s,fdr=a,power=pwr,pi0=p0,maxN=N,side="two-sided") os$ssize ##first sample sizes to reach desired power os$power ##calculated power for each sample size os$crit.vals ##calculated critical value for each sample sizeCalculates appropriate sample sizes for two-sample microarray experiments in which effect sizes as well as variances vary among genes. Sample sizes are determined based on a desired power, a controlled false discovery rate, and user-specified proportions of non-differentially expressed genes. Outputs a graph of power versus sample size. A graph of power versus sample size is created.
ssize.oneSampVary(deltaMean, deltaSE, a, b, fdr = 0.05, power = 0.8, pi0 = 0.95, maxN = 35, side = "two-sided", cex.title=1.15, cex.legend=1)dm<-2; ds<-1 ##the effect sizes of the genes follow a Normal(2,1) distribution alph<-3; beta<-1 ##the variances of the genes follow an Inverse Gamma(3,1) distribution. a2<-0.05 ##false discovery rate to be controlled pwr2<-0.8 ##desired power p0<-c(0.90,0.95,0.995) ##proportions of non-differentially expressed genes N1<-35 ##maximum sample size to be used in calculations osv<-ssize.oneSampVary(deltaMean=dm,deltaSE=ds,a=alph,b=beta,fdr=a2,power=pwr2,pi0=p0, maxN=N1,side="two-sided") osv$ssize ##first sample sizes to reach desired power osv$power ##calculated power for each sample size osv$crit.vals ##calculated critical value for each sample sizeCalculates appropriate sample sizes for two-sample microarray experiments for a desired power. Sample size calculations are performed at controlled false discovery rates, user-specified proportions of non-differentially expressed genes, effect size, and standard deviation. A graph of power versus sample size is created.
ssize.twoSamp(delta, sigma, fdr = 0.05, power = 0.8, pi0 = 0.95, maxN = 35, side = "two-sided", cex.title=1.15, cex.legend=1)##See Figure 1.(a) of Liu & Hwang (2007) d<-1 ##difference in differentially expressed genes to be detected s<-0.5 ##standard deviation a<-0.05 ##false discovery rate to be controlled pwr<-0.8 ##desired power p0<-c(0.5,0.9,0.95) ##proportions of non-differentially expressed genes N<-20 ##maximum sample size for calculations ts<-ssize.twoSamp(delta=d,sigma=s,fdr=a,power=pwr,pi0=p0,maxN=N,side="two-sided") ts$ssize ##first sample sizes to reach desired power for each proportion of ##non-differentially expressed genes ts$power ##calculated power for each sample size ts$crit.vals ##calculated critical value for each sample sizeCalculates appropriate sample sizes for two-sample microarray experiments in which the differences between mean treatment expression levels (delta.g for gene g) as well as standard deviations vary among genes. Sample sizes are determined based on a desired power, a controlled false discovery rate, and user-specified proportions of non-differentially expressed genes. A graph of power versus sample size is created.
ssize.twoSampVary(deltaMean, deltaSE, a, b, fdr = 0.05, power = 0.8, pi0 = 0.95, maxN = 35, side = "two-sided", cex.title=1.15, cex.legend=1)##See Figure 3.(a) of Liu & Hwang (2007) dm<-2; ds<-1 ##the delta.g's follow a Normal(2,1) distribution alph<-3; beta<-1 ##the variances of genes follow an Inverse Gamma(a,b) distribution a2<-0.05 ##false discovery rate to be controlled pwr2<-0.8 ##desired power p0<-c(0.90,0.95,0.995) ##proportions of non-differentially expressed genes N1<-35 ##maximum sample size to be used in calculations tsv<-ssize.twoSampVary(deltaMean=dm,deltaSE=ds,a=alph,b=beta,fdr=a2,power=pwr2,pi0=p0, maxN=N1,side="two-sided") tsv$ssize ##first sample size(s) to reach desired power tsv$power ##calculated power for each sample size tsv$crit.vals ##calculated critical value for each sample size| Repository | Version | Published | First seen | Last seen | Docs |
|---|---|---|---|---|---|
| CRAN | 1.3 | 2026-05-29 | 2026-05-30 |
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